Brightness and contrast correction for video extensometer systems and methods

ABSTRACT

The present disclosure describes systems and methods to correct for edge-position error associated with brightness levels of an associated back screen in a video extensometer system. In some examples, to correct for edge-position error, a processing system is configured to execute an edge detection algorithm to measure and/or calculate a difference between a perceived edge-position and a reference edge-position associated with an amount of error, and calculate a correction term to address the error. The correction term can be added to the result of the edge detection algorithm in case of white-to black transition, and subtracted in case of black-to-white transition to correct for the error.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Non-Provisional Patent Application which claimspriority to U.S. Provisional Patent Application No. 62/866,391, entitled“Brightness And Contrast Correction For Video Extensometer Systems AndMethods”, filed Jun. 25, 2019, the contents of which are hereinincorporated by reference in their entirety.

BACKGROUND

Camera based vision systems have been implemented as part of materialstesting systems, for measurement of specimen strain. These systemscollect one or more images of a specimen under test, with these imagesbeing synchronized with other signals of interest for the test (e.g.,specimen load, machine actuator/crosshead displacement etc.). The imagesof the test specimen can be analyzed to locate and track specificfeatures of the specimen as the test progresses. Changes in the locationof such features, such as a width of the specimen, allows local specimendeformation to be calculated and in turn specimen strain to be computed.

Conventional systems employ backlit screens and/or one or more lightsources to direct light onto multiple surface and/or sides of the testspecimen. However, edge-position error associated with brightness levelsof the backlit screen can lead to distorted readings and inaccuratemeasurements. Thus, a system to correct for such errors is desirable.

SUMMARY

Disclosed herein are systems and methods to correct for edge-positionerror associated with brightness levels of an associated back screen ina video extensometer system. In disclosed examples, one or more imageprocessing algorithms can be executed to measure a width of the testspecimen by identifying the transition edges of the specimen as theyappear as a dark silhouette in front of the illuminated back screen. Insome examples, to correct for edge-position error, a processing systemis configured to execute an edge detection algorithm to measure and/orcalculate a difference between a perceived edge-position and a referenceedge-position associated with an amount of error, and calculate acorrection term to address the error. The correction term can be appliedto one or more results of the algorithm to correct for the error. Insome examples, the correction term can be added to the result of theedge detection algorithm in case of white-to black transition, andsubtracted in case of black-to-white transition to correct for the error

These and other features and advantages of the present invention will beapparent from the following detailed description, in conjunction withthe appended claims.

In disclosed examples, a system for correcting brightness distortion ofa test specimen includes a testing system to secure a test specimen. Ascreen provides illumination to silhouette the test specimen, be itactively or passively illuminated. An imaging device, such as a videocamera, is arranged opposite the screen relative to the test specimenand configured to capture images of the test specimen. A processingsystem is configured to receive images of the test specimen from theimaging device, measure one or more characteristics at one or morepositions along an edge of the test specimen, compare the one or morecharacteristics to a reference characteristic, determine a correctiveterm based on the comparison, and apply the corrective term to the oneor more characteristics measurements to provide a corrected measurement.

In some examples, the correction term is added to the result of the edgedetection algorithm in case of white-to black transition. In someexamples, the correction term is subtracted in case of black-to-whitetransition to correct for the error. In some examples, the correctionterm is in one of millimeters, inches, or pixel units. In some examples,the one or more characteristics comprises one or more of an edgeposition or a width of the test specimen.

In some examples, the processor is located with a remote computingplatform in communication with one or more of the testing system or theimaging device.

In other disclosed examples, a method for correcting brightnessdistortion of a test specimen includes arranging a test specimen betweenan illuminated screen and an imaging device. A processing systemaccesses a list of correction terms wherein the correction terms are afunction of one or more characteristics including brightness and focus.The processing system determines a correction term from the list ofcorrection terms based on one of a predetermined focus or calculatedfocus of the imaging device. The imaging device images a silhouette ofthe test specimen against the illuminated screen. The processing systemcalculates one or more characteristic measurements based on the imagingand applies the corrective term to the one or more characteristicmeasurements of the test specimen to provide a corrected measurement.

In some examples, the one or more characteristics comprises one or moreof an edge position or a width of the test specimen. In some examples,the correction term is in one of millimeters, inches, or pixel units. Insome examples, the method corrects for distortions based on contrast ina captured image or a focus of the imaging system. In some examples, themethod includes modeling values associated with one or more ofbrightness, contrast, or focus to determine distortions associated withbrightness, contrast, or focus in the captured image; and outputting thecorrective term based on the distortions relative to the one or morecharacteristics.

BRIEF DESCRIPTION OF THE DRAWINGS

The benefits and advantages of the present invention will become morereadily apparent to those of ordinary skill in the relevant art afterreviewing the following detailed description and accompanying drawings,wherein:

FIG. 1 is a block diagram of an example extensometer system, inaccordance with aspects of this disclosure.

FIG. 2 is an example test specimen for measurement in the extensometersystem of FIG. 1 , in accordance with aspects of this disclosure.

FIG. 3 is a block diagram of an alternate view of the exampleextensometer system of FIG. 1 , in accordance with aspects of thisdisclosure.

FIG. 4 illustrates a captured image of a test specimen, in accordancewith aspects of this disclosure.

FIG. 5 illustrates an example image of a specimen subjected to opticaleffects of a shaded background.

FIG. 6 illustrates an example graph of relating perceived specimen widthto brightness, in accordance with aspects of this disclosure.

FIG. 7 is a look up table, in accordance with aspects of thisdisclosure.

FIG. 8 is a block diagram of an example implementation of theextensometer system of FIG. 1 .

FIG. 9 shows a flowchart representative of example machine-readableinstructions to correct for brightness distortion of a test specimen inan extensometer system, in accordance with aspects of this disclosure.

The figures are not necessarily to scale. Where appropriate, similar oridentical reference numbers are used to refer to similar or identicalcomponents.

DETAILED DESCRIPTION

The present disclosure describes systems and methods to correct foredge-position error associated with brightness levels of an associatedback screen in a video extensometer system. In some examples, to correctfor edge-position error, a processing system is configured to execute anedge detection algorithm to measure and/or calculate a differencebetween a perceived edge-position and a reference edge-positionassociated with an amount of error, and calculate a correction term toaddress the error. The correction term can be applied to one or moreresults of the algorithm to correct for the error. In some examples, thecorrection term can be added to the result of the edge detectionalgorithm in case of white-to black transition, and subtracted in caseof black-to-white transition to correct for the error.

As disclosed herein, a video extensometer system is configured toperform optical width measurement of a test specimen. In some examples,edges of a substantially non-transparent test specimen are measuredbased on a level of brightness contrast between the test specimen and aback screen. For examples, the test specimen it secured within a testingmachine and arranged in front of an illuminated (e.g., an actively orpassively lit) back screen. An imaging device is arranged to observe asurface of the test specimen that is facing the camera, the surfacebeing close to a focal plane of the imaging device optics (see, e.g.,FIG. 3 ). With this arrangement, the test specimen is viewed and imagedby the imaging device as a dark silhouetted shape, as it is located infront of a brightly illuminated back screen (see, e.g., FIG. 4 ).

For example, when arranged between the illuminated back screen and theimaging device, the distinctly focused dark silhouette of the testspecimen is stark, and the shape and character of the edges are welldefined when imaged in front of illuminated back screen. In someexamples, the test specimen is made of a material with greatertransparency. Such semi-transparent test specimens may absorb some ofthe light from the light source, sufficient to provide a measurablelevel of contrast between the test specimen and the back screen.

However, performing highly accurate measurements can be difficult, asperceived positions of the edge of the test specimen depends on thebrightness of the back screen, as well as on the focus of the imagingdevice.

As disclosed herein, to correct for edge-position error, a processingsystem is configured to execute an edge detection algorithm to measureand/or calculate a difference between a perceived edge-position and areference edge-position associated with an amount of error, andcalculate a correction term to address the error. For example, thecorrection term can be applied to one or more results of the algorithmto correct for the error. In some examples, the correction term can beadded to the result of the edge detection algorithm in case of white-toblack transition, and subtracted in case of black-to-white transition tocorrect for the error.

As described herein, material testing systems, including materialtesting systems that apply tension, compression, and/or torsion, includeone or more components that incur displacement and/or load bearing toapply and/or measure stresses on a test specimen. In some examples, avideo extensometer system is employed in specimen strain testing, whichcan include one or more of collecting high resolution images, providingthe images to an image processor, analyzing the images to identify oneor more specimen characteristics corresponding to displacement or strainvalue, and generating an output corresponding to the characteristics. Ina disclosed example, the identified characteristics (such as width) fromthe one or more collected images are compared against one or moresources, such as a list of threshold values or to an image collectedpreviously (i.e. prior to testing). In some examples, a value of theidentified characteristic may be applied to one or more algorithms togenerate an output corresponding to displacement or strain valueassociated with the test specimen.

Video processing that employs extensometers may include an externalmachine vision imaging device connected to a processing system orcomputing platform and/or video processing hardware and use softwareand/or hardware to convert the data from the camera to an electricalsignal or having a software interface compatible with the materialstesting system.

As disclosed herein, camera based image capture (e.g., vision or video)systems are implemented in materials testing systems for measurement ofstrain on the test specimen. Such systems collect multiple images of thespecimen under test (i.e. during a testing process), with the imagesbeing synchronized with other signals of interest for the test (such asspecimen load, machine actuator and/or crosshead displacement, etc.).The images of the specimen are analyzed (e.g., in real-time and/orpost-test) by algorithms to locate and track specific specimencharacteristics as the test progresses. For instance, a change in alocation, size, shape, etc., of such characteristics allows for testspecimen deformation to be calculated, which leads in turn to analysisand calculation of specimen strain.

Characteristics such as specimen width may be captured via an imagingdevice, with the captured image transmitted to a processing system.Image analysis can be performed by the extensometer system (e.g. via theprocessing system) to determine a first or initial position and/orlocation of the specimen width(s) to track changes in the width(s) asthe test progresses.

The image processing algorithms then determine the edges of the specimenand calculate the width of the specimen and track changes in specimenwidth compared to the initial width at the beginning of the test (i.e.transverse strain).

The processing system is configured to execute an edge detectionalgorithm to measure and/or calculate a difference between a perceivededge-position (from the captured images) and a reference edge-positionassociated with an amount of error, and calculate a correction term toaddress the error. The correction term can be applied to one or moreresults of the algorithm to correct for the error. In some examples, thecorrection term can be added to the result of the edge detectionalgorithm in case of white-to black transition, and subtracted in caseof black-to-white transition to correct for the error

As described herein, video extensometers that measure the width of thetest specimen require controlled background lighting conditions. This isachieved by including a backlight system, be it an active (separatelysourced) backlight or a passive backlight (employing reflected light).In the case of a video extensometer that will be used to measure thewidth of the specimen (transverse specimen edge based strain); there ispresently a limitation where brightness levels of the backgroundillumination result in errors for edge detection of a dark specimensilhouette.

Moreover, control of relative brightness levels of the back screen canbe achieved by adjustments in the absolute and/or relative componentpositions, angular orientations of the camera, the light source, testspecimen, and/or back screen, and/or adjustment to a power level orother illumination characteristic.

In disclosed examples, a system for correcting brightness, contrast, orfocus distortion of a test specimen includes a testing system to securea test specimen; a screen to provide illumination to silhouette the testspecimen; an imaging device arranged opposite the screen relative to thetest specimen and configured to capture images of the test specimen; anda processing system. The processing system is to receive images of thetest specimen from the imaging device; measure one or morecharacteristics at one or more positions along an edge of the testspecimen during a testing process; and compare the one or morecharacteristics to a reference characteristic.

In some examples, the correction term is added to the result of the edgedetection algorithm in case of white-to black transition. In examples,the correction term is subtracted in case of black-to-white transitionto correct for the error. In some examples, the correction term is inone of millimeters, inches, or pixel units.

In some examples, the one or more characteristics comprises one or moreof an edge position or a width of the test specimen. In examples, theedge position is referenced in pixel coordinates and corrected based ona direction of contrast, a level of contrast, or a level of brightnessand/or focus of the test specimen relative to the screen.

In some examples, the processor is located with a remote computingplatform in communication with one or more of the testing system or theimaging device.

In examples, the processor is integrated with one of the imaging deviceor the testing system. In some examples, the processor is furtherconfigured to determine a corrective term based on the comparison; andapply the corrective term to the one or more characteristicsmeasurements to provide a corrected measurement.

In some disclosed examples, a method for correcting brightness,contrast, or focus distortion of a test specimen is provided. The methodincludes arranging a test specimen between an illuminated screen and animaging device; imaging, via the imaging device, a silhouette of thetest specimen against the illuminated screen; calculating, via theprocessing system, one or more characteristic measurements based on theimaging; accessing, via a processing system, a list of correction terms,wherein the correction terms are a function of one or morecharacteristics including brightness, contrast and focus; anddetermining, via the processing system, a correction term from the listof correction terms based on one of a brightness, a predetermined focus,or calculated focus of the imaging device;

In some examples, the method includes applying, via the processingsystem, the corrective term to the one or more characteristicmeasurements of the test specimen to provide a corrected measurement.

In some examples, the one or more characteristics comprises one or moreof an edge position or a width of the test specimen. In examples, thecorrection term is in one of millimeters, inches, or pixel units. Inexamples, the method includes correcting for distortions based oncontrast in a captured image or a focus of the imaging system.

In some examples, the method includes modeling values associated withone or more of brightness, contrast or focus to determine distortionsassociated with brightness, contrast, or focus in the captured image;and outputting the corrective term based on the distortions relative tothe one or more characteristics.

In some examples, the imaging device is configured to capture polarizedlight or infrared light reflected from the screen or the test specimen,the screen reflecting light to create a dark silhouette of the testspecimen for edge analysis.

In some examples disclosed, a system for correcting brightnessdistortion of a test specimen is provided. The system includes aprocessing system to receive images from an imaging device of a testspecimen during a testing process, wherein the imaging device isarranged opposite a reflective screen relative to the test specimen;measure one or more characteristics at one or more positions along anedge of the test specimen during the testing process; determinedistortions along the edge of the test specimen in the images associatedwith brightness, contrast, or focus; and determine a corrective termbased on the distortions.

In some examples, the processing system outputs the corrective termbased on the distortions relative to the one or more characteristics. Inexamples, the processing system is further configured to apply thecorrective term to correct for distortions of the one or morecharacteristics based on one or more of brightness, contrast, or focusin the images.

In some examples, one or more light sources direct light to a surface ofthe test specimen and a reflective surface of the screen, wherein thetest specimen is arranged between the one or more light sources and thescreen.

Referring now to the figures, FIG. 1 is an example extensometer system10 to measure changes to one or more characteristics of a test specimen16 undergoing a mechanical property testing. The example extensometersystem 10 may be connected to, for example, a testing system 33 capableof mechanical testing of the test specimen 16. The extensometer system10 may measure and/or calculate changes in the test specimen 16subjected to, for example, compression strength testing, tensionstrength testing, shear strength testing, bend strength testing,deflection strength testing, tearing strength testing, peel strengthtesting (e.g., strength of an adhesive bond), torsional strengthtesting, and/or any other compressive and/or tensile testing.Additionally, or alternatively, the material extensometer system 10 mayperform dynamic testing.

In accordance with disclosed examples, the extensometer system 10 mayinclude the testing system 33 for manipulating and testing the testspecimen 16, and/or a computing device or processing system 32communicatively coupled to the testing system 33, the light source,and/or the imaging device, as further shown in FIG. 8 . The testingsystem 33 applies loads to the test specimen 16 and measures themechanical properties of the test, such as displacement of the testspecimen 16 and/or force applied to the test specimen 16.

The extensometer system 10 includes a remote and/or an integral lightsource 14 (e.g., an LED array) to illuminate the test specimen 16 and/ora reflective back screen 18. The extensometer system 10 includes aprocessing system 32 (see also FIG. 8 ) and a camera or imaging device12. In some examples, the light source 14 and the imaging device 12 areconfigured to transmit and receive in the infrared (IR) wavelengths;however, other wavelengths are similarly applicable. In some examples,one or both of the light source 14 or the imaging device 12 include oneor more filters (e.g., a polarizing filter), one or more lenses. In someexamples, a calibration routine is performed (e.g., a two-dimensionalcalibration routine) to identify one or more characteristics of the testspecimen 16, one or more markers 20 (including a pattern of markers), isadditionally used.

In some examples, the back screen 18 is configured to reflect light fromthe light source 14 back to the imaging device 12. For example, asurface of the back screen 18 may be configured with properties toenhance reflection and/or direct reflected light toward the imagingdevice. Properties can include a shape of the back screen 18 (e.g. in aparabolic configuration), and/or a treatment to increase reflection(e.g., application of cube corner reflectors, a reflective material,etc.). Additionally or alternatively, a filter 30 can be arranged and/orapplied to a surface to increase the amount of reflection and/or directreflected light in a desired direction and/or wavelength. In someexamples, the filter 30 is configured as a collimating filter, toprovide as much reflected light as possible toward the imaging device 12and away from other nearby components.

In disclosed examples, the computing device 32 may be used to configurethe testing system 33, control the testing system 33, and/or receivemeasurement data (e.g., transducer measurements such as force anddisplacement) and/or test results (e.g., peak force, break displacement,etc.) from the testing system 33 for processing, display, reporting,and/or any other desired purposes. The extensometer system 10 connectsto the 33 and software utilizing standard interfaces that includesEthernet, analog, encoder or SPI. This allows the device to be pluggedinto and used by existing systems without the need for specializedintegration software or hardware. The extensometer system 10 providesaxial and transverse encoder or analog information in real-time tomaterials testing machine 33. Real-time video extensometer 10 andmaterials testing machine 190 exchange real-time test data, includingextension/strain data, with the external computer 32, which may beconfigured via a wired and/or wireless communications channel. Theextensometer system 10 provides measurement and/or calculation ofextension/strain data captured from the test specimen 16 subjected totesting in the materials testing machine 33, which in turn, providesstress and extension/strain data to the processor 32.

As disclosed herein, the captured images are input to the processor 32from the imaging device, where one or more algorithms and/or look uptables are employed to calculate multiple axes of extension/strainvalues for the test specimen 16 (i.e., the change or percentage changein inter-target distance as calculated by image monitoring of themarkers 20 affixed to the test specimen 16). Following computation, thedata may be stored in memory or output to a network and/or one or moredisplay devices, I/O devices, etc. (see also FIG. 8 ).

FIG. 2 is an example test specimen 16 for measurement in theextensometer system 10 of FIG. 1 . For example, one or more markings areapplied to the surface 28 facing the light source 14 and imaging device12. Grip sections 26 are configured for placement within a grip of thetesting system 33 (see also FIG. 8 ), and apply force to the testspecimen 16. For example, a cross-member loader applies force to thespecimen 16 under test, while the grips grasp or otherwise couple thetest specimen 16 to the testing system 33. A force applicator such as amotor causes the crosshead to move with respect to the frame to applyforce to the test specimen 16, as illustrated by double arrow 34. Forces34 pulling the grip sections 26 away from one another may elongate thetest specimen 16, resulting in the markings moving from a first position20A to a second position 20B. Additionally or alternatively, themarkings may change shape or size, which may also be measured by theprocessing system 32 in view of the captured images. The forces 34 mayalso cause the edges of the test specimen to move from a first position22A to a second position 22B. For example, at the first or initialposition, the edges have a width 24A, which is reduced to width 24B uponapplication of the forces 34.

Based on the captured images, the processing system 33 is configured toimplement an extension/strain on measurement process. For example, todetect an extension/strain on the test specimen 16, the processingsystem 33 monitors the images provided via the imaging device 12. Whenthe processing system 33 identifies a change in relative positionbetween two or more of the markers and/or the edges of the test specimen16 (e.g., compared to an initial location at a beginning of movement ofthe crosshead), the processing system 33 measures the amount of changeto calculate the amount of extension and/or strain on the test specimen16. As disclosed herein, the markers are configured to reflect lightfrom the light source to the camera, whereas the back screen reflectslight to create a dark silhouette for edge analysis.

As disclosed herein, the video extensometer system 10 is configured toperform optical width measurement of non-transparent test specimen 16.The imaging device 12 is arranged to observe the surface 28 of the testspecimen 16 that is facing the imaging device 12, the surface 28 beingclose to a focal plane of the imaging device optics (see, e.g., FIG. 3). With this arrangement, the test specimen 16 is viewed and imaged bythe imaging device 12 as a dark silhouetted shape, as it is located infront of the brightly illuminated back screen 18.

For example, when arranged between the illuminated back screen 18 andthe imaging device 12, the distinctly focused dark silhouette of thetest specimen 16 is stark, and the shape and character of the edges 22are well defined when imaged in front of illuminated back screen 18.However, performing highly accurate measurements can be difficult, asthe perceived position of the edges 22 of the test specimen 16 dependson the brightness of the back screen 18, as well as on the focus of theimaging device 12.

As disclosed herein, to correct for edge-position error, the processingsystem 32 is configured to execute an edge detection algorithm tomeasure and/or calculate a difference between a perceived edge-positionand a reference edge-position associated with an amount of error, andcalculate a correction term to address the error. For example, thecorrection term is applied to one or more results of the algorithm tocorrect for the error. In some examples, the correction term can beadded to the result of the edge detection algorithm in case of white-toblack transition, and subtracted in case of black-to-white transition tocorrect for the error.

FIG. 3 shows an arrangement for a video extensometer system 10 tomeasure one or both of axial strain (based on changes in markers 20and/or a pattern of markers on the test specimen 16 front surface 28),and transverse strain (calculated from changes in width of the specimen16). The components of the video extensometer system 10 are shown in atop perspective in FIG. 3B, with general locations of each componentrelative to the others. As shown, the components include an imagingdevice 12 (e.g., a video camera) configured to capture one or moreimages of the test specimen 16 during the physical test (e.g., atregular intervals, continuously, and/or based on one or more thresholdvalues associated with time, force, or other suitable testcharacteristic).

One or more light sources 14 emit light 36 to illuminate a surface 28 ofthe test specimen 16 and a screen 18 that is arranged facing a rearsurface of the test specimen 16 opposite the light source 14. In someexamples, the light source(s) 14 are arranged to direct light off-axis(e.g., in an upwards, sideways, and/or downwards direction shown from atop elevation in view of FIG. 3 ), and angled to illuminate the frontsurface 28 of the test specimen 16 and/or the back screen 18.

As shown, a passive (i.e. lacking active illumination source) backscreen 18 is arranged to the rear of the test specimen 16, designed withreflective properties and of a size suitable to present a uniformlybright background to the video extensometer imaging device 12. As shownin FIG. 3 , light 36 incident on back screen 18 is reflected back aslight 40 directed toward imaging device 12. In some examples, anactively illuminated back screen is used, the brightness level of whichcan be adjusted by the processing system 32. As shown, the imagingdevice 12 and test specimen 16 are arranged at a focal distance 39,which during the testing process may be static, predetermined, and/orchanging. The light from the back screen 18 creates a darkenedsilhouette of the test specimen 16, allowing the imaging device 12 tocapture images of the edges 22, and changes thereof, during the testingprocess.

The test specimen 16 located between the imaging device 12 and the backscreen 18. The test specimen 16 features suitable marks 20 on the frontfacing surface 28 of the test specimen 16. Analysis of the one or moreimages associated with the video extensometer system 10 is implementedvia processing system 32 to perform identification algorithms that allowboth the test specimen 16 markings 20 and the test specimen edges 22 tobe continuously tracked and measured during the test process.

FIG. 4 illustrates a captured image of a test specimen 16. As shown, thedistinctly focused dark silhouette of test specimen 16 is stark, and theshape and character of the edges 22 are well defined when imaged infront of illuminated back screen 18. The brightness of the background inFIG. 4 is shown as substantially evenly distributed. However, for abackground of variable brightness, a more brightly illuminatedbackground makes the edges appear closer to dark areas relative to lessbrightly illuminated areas of the background. As illustrated in FIG. 5 ,a rectangular specimen is arranged on a background where brightnessvaries from the center outwards. Thus, as illustrated, brighter portionsof the background make the specimen appear thinner. This phenomenonholds even if the test system is carefully calibrated. The resultingedge-position error is therefore defined as a difference betweenperceived edge-position and reference edge-position in pixel units forwhite-to-black transition.

In order to achieve highly accurate measurements, a correction term canbe calculated and applied, to correct for error between a perceivedposition of the edge of the test specimen depends on the brightness ofthe back screen, as well as on the focus of the imaging device.

A graph is shown in FIG. 6 , associating a perceived change in the widthof a test specimen with the width measured at a reference brightness.For example, an error associated with width measurement may correspondto one or both of the level of brightness of the back screen, and alevel of sensitivity of an edge detection algorithm with respect to thebrightness of the back screen. Such errors may result in an aggregatedcontribution error, such as during a testing process where width isimaged and recorded multiple times over a given time period during whichthe brightness of the back screen changes. In some examples, the testspecimen is controlled to move in axial direction (e.g. the test systemmoves the test specimen vertically) in an area where the back screen isilluminated at varying levels over the axil movement.

As disclosed herein, to correct for edge-position error, the processingsystem 32 is configured to execute an edge detection algorithm tomeasure and/or calculate a difference between a perceived edge-positionand a reference edge-position associated with an amount of error, andcalculate a correction term to address the error. For example, thecorrection term is applied to one or more results of the algorithm tocorrect for the error. In some examples, the correction term can beadded to the result of the edge detection algorithm in case of white-toblack transition, and subtracted in case of black-to-white transition tocorrect for the error.

In some examples, the result of the edge detection algorithm maycorrespond to one or more corrective actions, such as a command tocontrol adjustment of the brightness level of the back screen, a focusof the imaging device, a position of one or more components, forinstance.

In correcting for brightness errors, the extensometer system 10 maycalibrate the imaging device 12, such that the width of the testspecimen can be determined as the width of the shadow cast by the testspecimen 16 onto a sensor of the imaging device 12 (e.g., a photodiode,etc.). An edge is defined as the region of the image where the darksilhouette ends and the bright background appears. An edge detectionalgorithm can be executed to determine a position of the test specimenedge to sub-pixel accuracy.

Based on the determined a position and/or character of one or more edgesof the test specimen, the processing system 32 executes an edgedetection algorithm to measure and/or calculate a difference between aperceived edge-position (e.g., what is captured by the imaging device12) and a reference edge-position associated with an amount of error(e.g., what is expected from test specimen measurements), and calculatea correction term to address the error. For example, to correct foredge-position error, the correction term can be applied to one or moreresults of the algorithm to correct for the error. In some examples, thecorrection term can be added to the width determined from the edgedetection algorithm in case of white-to black transition, and subtractedin case of black-to-white transition to correct for the error. Forinstance, the edge position, referenced in pixel coordinates, iscorrected based on a direction of contrast, a level of contrast, or alevel of brightness and/or focus. In some examples, the measurementsand/or position of the one or more edges are provided in pixelcoordinates, as captured by the imaging device 12. Additionally oralternatively, the measurements and/or position of the one or more edgesare provided in other standard coordinate systems/units, such as meters.In such an example, a calibration process can be implemented todetermine absolute and/or relative placement and/or dimensions of thetest specimen within the test system prior to measurement. Moreover,width measurement may be employed to determine error, such as when oneor more relevant parameters (e.g., contrast, focus, etc.) are availableand captured for both edges of the test specimen (e.g., to perform acomparison of edge characteristics).

When performing highly accurate measurements, a perceived position ofthe edge depends on brightness of background, as illustrated withrespect to FIG. 5 , as well as on focus of the camera. The errorassociated with background brightness, however, is well defined for aknown focus and for known background brightness. Thus, a listing ofvalues can be generated that contains the edge-position error forrelevant focus and background brightness configurations. In the exampleof FIG. 7 , the listing may store the values as a two-dimensional lookuptable.

The listing may contain adjusted correction terms (e.g., additive,subtractive, multipliers, etc.), for given brightness scores and/orgiven focus scores (e.g., in standard or relative units, including analgorithm generated integer(s)), for example. The correction terms canbe generated as a function of one or more characteristics, includingbrightness and focus, as well as other values relating to imagedistortion. In some examples, the correction term may be calculated tothe edge-position error multiplied by minus one (−1).

During a measurement and/or testing process, the processing system 32 isconfigured to generate an edge detection brightness score and/or a focusscore. Based on the results, an associated correction term can bedetermined by accessing the two-dimensional lookup table. Typically,linear interpolation (and/or extrapolation) would be used to determinecorrection terms not explicitly listed in that table. As disclosedherein, the correction term would then be added to the result of edgedetection algorithm in case of white-to black transition, and it wouldbe subtracted in case of black-to-white transition. Additionally oralternatively, the correction term can be calculated in real-time (e.g.,during a measurement process, a testing process, a calibration process,etc.) based on a model (e.g., an algorithm) and/or an analyticdescription of the process. Model parameters (e.g., proportionalityfactors) can be employed, which may be hard-coded and applied as needed.For example, values associated with focus, contrast, and/or brightnesscould be input to the model or could be hard-coded.

In some examples, measures of one or both of brightness and focus may beknown in advance. In this example, the test specimen 16 may remain inthe same optical plane during measurement and/or the testing process.The associated focus score can be determined in advance and appliedprior to measurements. Additionally or alternatively, the lookup tablecan be simplified to a one-dimensional lookup table. In other words, theone-dimensional lookup table contains correction terms for differentbrightness scores for the per-determined focus score. In some examples,a listing or matrix of predetermined focus scores can be generated bythe processing system, stored in memory, and accessed and applied forthe corresponding testing process.

An example application that would benefit from use of the disclosessystems and methods would be R-value measurement in materials testing,where the test specimen moves relative to a background withnon-homogeneous brightness (as illustrated in FIG. 5 ) and width-changeis being recorded.

FIG. 8 is a block diagram of an example extensometer system 10 of FIG. 1. As shown in FIG. 1 , the extensometer system 10 includes the testingsystem 33 and the computing device 32. The example computing device 32may be a general-purpose computer, a laptop computer, a tablet computer,a mobile device, a server, an all-in-one computer, and/or any other typeof computing device. The computing device 32 of FIG. 8 includes aprocessor 202, which may be a general-purpose central processing unit(CPU). In some examples, the processor 202 may include one or morespecialized processing units, such as FPGA, RISC processors with an ARMcore, graphic processing units, digital signal processors, and/orsystem-on-chips (SoC). The processor 202 executes machine-readableinstructions 204 that may be stored locally at the processor (e.g., inan included cache or SoC), in a random access memory 206 (or othervolatile memory), in a read-only memory 208 (or other non-volatilememory such as FLASH memory), and/or in a mass storage device 210. Theexample mass storage device 210 may be a hard drive, a solid-statestorage drive, a hybrid drive, a RAID array, and/or any other mass datastorage device. A bus 212 enables communications between the processor202, the RAM 206, the ROM 208, the mass storage device 210, a networkinterface 214, and/or an input/output interface 216.

An example network interface 214 includes hardware, firmware, and/orsoftware to connect the computing device 201 to a communications network218 such as the Internet. For example, the network interface 214 mayinclude IEEE 202.X-compliant wireless and/or wired communicationshardware for transmitting and/or receiving communications.

An example I/O interface 216 of FIG. 8 includes hardware, firmware,and/or software to connect one or more input/output devices 220 to theprocessor 202 for providing input to the processor 202 and/or providingoutput from the processor 202. For example, the I/O interface 216 mayinclude a graphics-processing unit for interfacing with a displaydevice, a universal serial bus port for interfacing with one or moreUSB-compliant devices, a FireWire, a field bus, and/or any other type ofinterface. The example extensometer system 10 includes a display device224 (e.g., an LCD screen) coupled to the I/O interface 216. Otherexample I/O device(s) 220 may include a keyboard, a keypad, a mouse, atrackball, a pointing device, a microphone, an audio speaker, a displaydevice, an optical media drive, a multi-touch touch screen, a gesturerecognition interface, a magnetic media drive, and/or any other type ofinput and/or output device.

The computing device 32 may access a non-transitory machine-readablemedium 222 via the I/O interface 216 and/or the I/O device(s) 220.Examples of the machine-readable medium 222 of FIG. 8 include opticaldiscs (e.g., compact discs (CDs), digital versatile/video discs (DVDs),Blu-ray discs, etc.), magnetic media (e.g., floppy disks), portablestorage media (e.g., portable flash drives, secure digital (SD) cards,etc.), and/or any other type of removable and/or installedmachine-readable media.

The extensometer system 10 further includes the testing system 33coupled to the computing device 32. In the example of FIG. 8 , thetesting system 33 is coupled to the computing device via the I/Ointerface 216, such as via a USB port, a Thunderbolt port, a FireWire(IEEE 1394) port, and/or any other type serial or parallel data port. Insome examples, the testing system 33 is coupled to the network interface214 and/or to the I/O interface 216 via a wired or wireless connection(e.g., Ethernet, Wi-Fi, etc.), either directly or via the network 218.

The testing system 33 includes a frame 228, a load cell 230, adisplacement transducer 232, a cross-member loader 234, materialfixtures 236, and a control processor 238. The frame 228 provides rigidstructural support for the other components of the testing system 33that perform the test. The load cell 230 measures force applied to amaterial under test by the cross-member loader 234 via the grips 236.The cross-member loader 234 applies force to the material under test,while the material fixtures 236 (also referred to as grips) grasp orotherwise couple the material under test to the cross-member loader 234.The example cross-member loader 234 includes a motor 242 (or otheractuator) and a crosshead 244. As used herein, a “crosshead” refers to acomponent of a material testing system that applies directional (axial)and/or rotational force to a specimen. A material testing system mayhave one or more crossheads, and the crosshead(s) may be located in anyappropriate position and/or orientation in the material testing system.The crosshead 244 couples the material fixtures 236 to the frame 228,and the motor 242 causes the crosshead to move with respect to the frameto position the material fixtures 236 and/or to apply force to thematerial under test. Example actuators that may be used to provide forceand/or motion of a component of the extensometer system 10 includeelectric motors, pneumatic actuators, hydraulic actuators, piezoelectricactuators, relays, and/or switches.

While the example testing system 33 uses a motor 242, such as a servo ordirect-drive linear motor, other systems may use different types ofactuators. For example, hydraulic actuators, pneumatic actuators, and/orany other type of actuator may be used based on the requirements of thesystem.

Example grips 236 include compression platens, jaws or other types offixtures, depending on the mechanical property being tested and/or thematerial under test. The grips 236 may be manually configured,controlled via manual input, and/or automatically controlled by thecontrol processor 238. The crosshead 244 and the grips 236 areoperator-accessible components.

The extensometer system 10 may further include one or more controlpanels 250, including one or more mode switches 252. The mode switches252 may include buttons, switches, and/or other input devices located onan operator control panel. For example, the mode switches 252 mayinclude buttons that control the motor 242 to jog (e.g., position) thecrosshead 244 at a particular position on the frame 228, switches (e.g.,foot switches) that control the grip actuators 246 to close or open thepneumatic grips 248, and/or any other input devices to control operationof the testing system 33.

The example control processor 238 communicates with the computing device32 to, for example, receive test parameters from the computing device 32and/or report measurements and/or other results to the computing device32. For example, the control processor 238 may include one or morecommunication or I/O interfaces to enable communication with thecomputing device 32. The control processor 238 may control thecross-member loader 234 to increase or decrease applied force, controlthe fixture(s) 236 to grasp or release a material under test, and/orreceive measurements from the displacement transducer 232, the load cell230 and/or other transducers.

The example control processor 238 is configured to implement anextension/strain measurement process when a test specimen 16 issubjected to testing in the testing system 33. For example, to detect anextension/strain on the test specimen 16, the control processor 238monitors the images provided via the imaging device 12. When the controlprocessor 238 identifies a change in location and/or position of theedges 22 of the test specimen 16 (e.g., compared to an initial locationat a beginning of movement of the crosshead 244), the control processor238 measures the amount of change to calculate the amount of extensionand/or strain on the test specimen 16. For example, real-time videoprovided by the imaging device 12 captures the absolute position ofedges 22, and monitors their relative movement over the course of theseveral images to calculate extension/strain in real time. The stressdata and the strain data exchanged among the real-time videoextensometer 10, the testing system 33 and the processing system 32, andtypically organized and displayed via the display device 224.

FIG. 9 shows a flowchart representative of example machine readableinstructions 300 which may be executed by the processing system 32 ofFIGS. 1 and 8 to correct for brightness distortion of a test specimen inan extensometer system. At block 302, a test specimen is arrangedbetween an illuminated screen and an imaging device. At block 304, theimaging device 12 images a silhouette of the test specimen against theilluminated screen. At block 306, the processing system 32 calculatesone or more characteristics measurements based on the imaging. At block308, the processing system 32 accesses a list of correction terms,wherein the correction terms are a function of one or morecharacteristics including brightness and focus. At block 310, theprocessing system 32 determines a correction term from the list ofcorrection terms based on a brightness, a predetermined focus, orcalculated focus of the imaging device. In addition, at block 312 theprocessing system 32 applies the corrective term to the one or morecharacteristics measurements of the test specimen to provide a correctedmeasurement.

The present methods and systems may be realized in hardware, software,and/or a combination of hardware and software. The present methodsand/or systems may be realized in a centralized fashion in at least onecomputing system, or in a distributed fashion where different elementsare spread across several interconnected computing systems. Any kind ofcomputing system or other apparatus adapted for carrying out the methodsdescribed herein is suited. A typical combination of hardware andsoftware may include a general-purpose computing system with a programor other code that, when being loaded and executed, controls thecomputing system such that it carries out the methods described herein.Another typical implementation may comprise an application specificintegrated circuit or chip. Some implementations may comprise anon-transitory machine-readable (e.g., computer-readable) medium (e.g.,FLASH drive, optical disk, magnetic storage disk, or the like) havingstored thereon one or more lines of code executable by a machine,thereby causing the machine to perform processes as described herein. Asused herein, the term “non-transitory machine-readable medium” isdefined to include all types of machine-readable storage media and toexclude propagating signals.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (i.e. hardware) and any software and/orfirmware (“code”) which may configure the hardware, be executed by thehardware, and or otherwise be associated with the hardware. As usedherein, for example, a particular processor and memory may comprise afirst “circuit” when executing a first one or more lines of code and maycomprise a second “circuit” when executing a second one or more lines ofcode. As utilized herein, “and/or” means any one or more of the items inthe list joined by “and/or”. As an example, “x and/or y” means anyelement of the three-element set {(x), (y), (x, y)}. In other words, “xand/or y” means “one or both of x and y”. As another example, “x, y,and/or z” means any element of the seven-element set {(x), (y), (z), (x,y), (x, z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means“one or more of x, y and z”. As utilized herein, the term “exemplary”means serving as a non-limiting example, instance, or illustration. Asutilized herein, the terms “e.g.,” and “for example” set off lists ofone or more non-limiting examples, instances, or illustrations. Asutilized herein, circuitry is “operable” to perform a function wheneverthe circuitry comprises the necessary hardware and code (if any isnecessary) to perform the function, regardless of whether performance ofthe function is disabled or not enabled (e.g., by a user-configurablesetting, factory trim, etc.).

While the present method and/or system has been described with referenceto certain implementations, it will be understood by those skilled inthe art that various changes may be made and equivalents may besubstituted without departing from the scope of the present methodand/or system. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the presentdisclosure without departing from its scope. For example, systems,blocks, and/or other components of disclosed examples may be combined,divided, re-arranged, and/or otherwise modified. Therefore, the presentmethod and/or system are not limited to the particular implementationsdisclosed. Instead, the present method and/or system will include allimplementations falling within the scope of the appended claims, bothliterally and under the doctrine of equivalents.

What is claimed is:
 1. A system for correcting brightness, contrast, orfocus distortion of a test specimen comprising: a testing system tosecure the test specimen; a screen to provide illumination to silhouettethe test specimen; an imaging device arranged opposite the screenrelative to the test specimen and configured to capture images of thetest specimen; and a processing system to: receive the images of thetest specimen from the imaging device; measure one or morecharacteristics at one or more positions along an edge of the testspecimen based on the images from the imaging device during a testingprocess; compare the one or more characteristics to a referencecharacteristic; determine a correction term based on the comparison, agiven brightness score for the screen relative to a level of brightnessof the test specimen obtained from the images from the imaging device,and a given focus score of the test specimen based on a level of focusof the test specimen relative to the screen obtained from the images ofthe imaging device; and apply the correction term to the measure of theone or more characteristics to provide a corrected measurement.
 2. Thesystem of claim 1, wherein the correction term is in one of millimeters,inches, or pixel units.
 3. The system of claim 1, wherein the one ormore characteristics comprises one or more of an edge position or awidth of the test specimen.
 4. The system of claim 3, wherein the edgeposition is referenced in pixel coordinates and corrected based on adirection of contrast, a level of contrast, or the level of brightnessand/or the level of focus of the test specimen relative to the screen.5. The system of claim 1, wherein the processor is located with a remotecomputing platform in communication with one or more of the testingsystem or the imaging device.
 6. The system of claim 1, wherein theprocessor is integrated with one of the imaging device or the testingsystem.
 7. A method for correcting brightness, contrast, or focusdistortion of a test specimen, the method comprising: arranging the testspecimen between an illuminated screen and an imaging device; imaging,via the imaging device, a silhouette of the test specimen against theilluminated screen; calculating, via the processing system, one or morecharacteristic measurements based on the imaging; accessing, via aprocessing system, a list of correction terms, wherein the correctionterms are a function of one or more characteristics includingbrightness, contrast and focus; and determining, via the processingsystem, a correction term from the list of correction terms based on aknown level of brightness of the illuminated screen and one of a knownfocus of the imaging device, or a calculated focus of the imagingdevice.
 8. The method of claim 7, further comprising applying, via theprocessing system, the correction term to the one or more characteristicmeasurements of the test specimen to provide a corrected measurement. 9.The method of claim 7, wherein the one or more characteristics comprisesone or more of an edge position or a width of the test specimen.
 10. Themethod of claim 7, wherein the correction term is in one of millimeters,inches, or pixel units.
 11. The method of claim 7, further comprisingcorrecting for distortions based on contrast in a captured image or afocus of the imaging system.
 12. The method of claim 11, furthercomprising: modeling values associated with one or more of brightness,contrast or focus to determine distortions associated with brightness,contrast, or focus in the captured image; and outputting the correctionterm based on the distortions relative to the one or morecharacteristics.
 13. The method of claim 7, wherein the imaging deviceis configured to capture polarized light or infrared light reflectedfrom the screen or the test specimen, wherein the screen reflects lightto create a dark silhouette of the test specimen for edge analysis. 14.A system for correcting brightness distortion of a test specimencomprising a processing system configured to: receive images from animaging device of the test specimen during a testing process, whereinthe imaging device is arranged opposite a reflective screen relative tothe test specimen; measure one or more characteristics at one or morepositions along an edge of the test specimen based on the images fromthe imaging device during the testing process; determine distortionsalong the edge of the test specimen in the images associated withbrightness, contrast, or focus; and determine a corrective term based onthe distortions, a given brightness score of the reflective screenrelative to a level of brightness of the test specimen obtained from theimages from the imaging device, and a given focus score of the testspecimen based on a level of focus of the test specimen relative to thereflective screen obtained from the images of the imaging device. 15.The system of claim 14, wherein the processing system is furtherconfigured to output the corrective term based on the distortionsrelative to the one or more characteristics.
 16. The system of claim 15,wherein the processing system is further configured to apply thecorrective term to correct for distortions of the one or morecharacteristics based on one or more of the level of brightness,contrast, or the level of focus in the images.
 17. The system of claim14, wherein one or more light sources direct light to a surface of thetest specimen and a reflective surface of the screen, wherein the testspecimen is arranged between the one or more light sources and thescreen.